Data deduplication parameter computation

ABSTRACT

In some examples, a system computes respective values for corresponding data value indicators added to and removed from a deduplication data store in which duplicated data values have been eliminated, where each respective data value indicator of the data value indicators represents presence of a unique data value in the deduplication data store. The system updates an estimator based on the respective values, to reflect an addition of a first data value indicator to the deduplication data store and a removal of a second data value indicator from the deduplication data store. The system computes, using the updated estimator, a parameter relating to data deduplication at the deduplication data store.

BACKGROUND

A data storage system can be used to store data. In some examples, datadeduplication can be applied to reduce an amount of data stored by thedata storage system. Data deduplication identifies data values that areduplicative, and seeks to eliminate instances of duplicative data valuesthat are stored in the data storage system.

BRIEF DESCRIPTION OF THE DRAWINGS

Some implementations of the present disclosure are described withrespect to the following figures.

FIG. 1 is a block diagram of an arrangement that includes a storagecontroller and a persistent storage, according to some examples.

FIG. 2 is a block diagram of an arrangement that includes data volumesand respective probabilistic cardinality estimators, according to someexamples.

FIGS. 3A-3G illustrate contents of a probabilistic cardinality estimatorupdated by respective operations, according to some examples.

FIG. 4 is a block diagram of a storage medium storing machine-readableinstructions, according to some examples.

FIG. 5 is a block diagram of a system according to some examples.

FIG. 6 is a flow diagram of a process of computing a data de-duplicationparameter, according to some examples.

Throughout the drawings, identical reference numbers designate similar,but not necessarily identical, elements. The figures are not necessarilyto scale, and the size of some parts may be exaggerated to more clearlyillustrate the example shown. Moreover, the drawings provide examplesand/or implementations consistent with the description; however, thedescription is not limited to the examples and/or implementationsprovided in the drawings.

DETAILED DESCRIPTION

In the present disclosure, use of the term “a,” “an”, or “the” isintended to include the plural forms as well, unless the context clearlyindicates otherwise. Also, the term “includes,” “including,”“comprises,” “comprising,” “have,” or “having” when used in thisdisclosure specifies the presence of the stated elements, but do notpreclude the presence or addition of other elements.

A data storage system can store data in multiple data volumes. Examplesof data storage systems include storage arrays, storage appliances, andso forth. A “data volume” refers to a logical collection of data.Logical data blocks can be written to a data volume, in response towrite requests from a requester device, such as a server computer, adesktop computer, a notebook computer, a tablet computer, a smartphone,or any other type of electronic device.

The data storage system can perform deduplication of data to reduce oravoid storing of duplicate data values in physical storage media of thedata storage system. A “physical storage media” can include a physicalstorage device or a number of physical storage devices. Examples ofphysical storage devices include disk-based storage devices, solid statedrives, and so forth.

Write requests received by the data storage system are to write logicaldata blocks to a data volume, or alternatively, to multiple datavolumes. Prior to storing a data value of each logical data block to thephysical storage media of the data storage system, the data storagesystem can make a determination of whether the data value of the logicaldata block is already stored in the data storage system. If so, then thedata storage system can elect to not store the data value again (toavoid storing a duplicate of the data value).

In a data storage system with a large number of data volumes where somedata volumes may be large, it can be challenging to determine aparameter relating to data deduplication on a per data volume basis. Asan example, the parameter can be a deduplication ratio of an individualdata volume, which is computed based on a ratio of a quantity of logicaldata blocks written to the individual data volume, to a quantity ofunique data values in the data volume.

Another example parameter of interest is a parameter regarding how manyphysical blocks can be reclaimed if a data volume were deleted. A“physical block” can refer to a region in the physical storage media ofa data storage system, where the region stores a data value. Regions inthe physical storage media that make up respective physical blocks caneach have a specified fixed size or may have variable sizes.

A further example parameter of interest is a parameter regarding asimilarity of multiple data volumes, such as based on a number of shareddata values.

In further examples, other parameters relating to data deduplication canbe computed on a per data volume basis. More generally, a parameterrelating to data deduplication can provide an indication of any or somecombination of the following: how effective data deduplication is inreducing the amount of data stored in a data volume, how much physicalstorage space is taken up by a data volume, how similar data volumes arein data deduplication performance, and so forth.

In accordance with some implementations of the present disclosure, aparameter relating to data deduplication in a data storage system can becomputed using a probabilistic cardinality estimator. For example, theprobabilistic cardinality estimator can be based on a probabilisticcardinality estimator used by a HyperLogLog algorithm for approximatinga number of distinct elements in a data set. In other examples, othertypes of probabilistic cardinality estimators for estimating a number ofdistinct data values in a data set can be employed.

The computation of a parameter relating to data deduplication can beperformed while the data storage system is online, i.e., the datastorage system is active in storing data and/or deleting data inresponse to incoming requests from a requester device (or alternatively,from multiple requester devices). To enable online computations ofparameters relating to data deduplication, the probabilistic cardinalityestimator is updated responsive to both additions and removals of datavalues in the data storage system.

FIG. 1 is a block diagram of an example arrangement that includes a datastorage system 102 that is coupled over a network 104 to requesterdevices 106. Examples of the network 104 include a local area network(LAN), a wide area network (WAN), a storage area network (SAN), or anyother type of network (whether wired or wireless).

The data storage system 102 includes a storage controller 108 and apersistent storage 110. The storage controller 108 manages access (reador write) of data values 112 stored at the persistent storage 110 (ormore specifically, stored on physical storage media of the persistentstorage 110).

A “controller” can refer to a hardware processing circuit, which caninclude any or some combination of a microprocessor, a core of amulti-core microprocessor, a microcontroller, a programmable integratedcircuit, a programmable gate array, a digital signal processor, oranother hardware processing circuit. Alternatively, a “controller” canrefer to a combination of a hardware processing circuit andmachine-readable instructions (software and/or firmware) executable onthe hardware processing circuit.

The persistent storage 110 can be implemented using any or somecombination of persistent (e.g., nonvolatile) storage device(s), such asdisk-based storage device(s) (e.g., hard disk drive(s) (HDDs)), solidstate device(s) (SSDs) (e.g., flash storage device(s)), or the like, ora combination thereof.

In response to write requests received at the data storage system 102,such as from a requester device 106 or from multiple requester devices106, incoming data values 114 associated with the write requests arereceived by the storage controller 108. The write requests are requeststo write logical data blocks to a data volume (or multiple datavolumes). The incoming data values 114 are the data values that are inthe logical data blocks written to the data volume(s).

In addition to write requests, the requester devices 106 can also submitread requests to the data storage system 102 to read logical data blocksof the data volume(s), which causes retrieval of corresponding datavalues 112 stored at the persistent storage 110.

Further, the requester devices 106 can submit requests that causedeletion of data values 112 stored at the persistent storage 110.

The storage controller 108 includes a deduplication engine 116, a datadeduplication parameter computation engine 120, and a merge engine 130.As used here, an “engine” can refer to a portion of the hardwareprocessing circuit of the storage controller 108, or to machine-readableinstructions executable by the storage controller 108.

The deduplication engine 116 performs data deduplication for theincoming data values 114. The deduplication engine 116 includes a datavalue fingerprint generator 118 and a block index update logic 122. Thedata value fingerprint generator 118 and the block index update logic122 can each be part of the hardware processing circuit ormachine-readable instructions of the deduplication engine 116. Althoughdepicted as being part of the deduplication engine 116, the data valuefingerprint generator 118 and/or the block index update logic 122 can beseparate from the deduplication engine 116.

The data value fingerprint generator 118 compute fingerprints based onthe incoming data values 114 (fingerprints are explained further below).The block index update logic 122 is used to update a cached block index124.

In some examples, a “block index” refers to mapping information thatmaps addresses identifying logical data blocks (referred to as logicalblock addresses or LBAs) to corresponding storage location indicators. Astorage location indicator provides an indication of a physical storagelocation storing the data value in the physical storage media. Anexample of a storage location indicator is a sequential block number(SBN).

An SBN is useable to indicate a physical storage location of a datavalue 112 stored at the persistent storage 110. However, in someexamples, the SBN does not actually identify the physical storagelocation, but rather, the SBN can be used to derive a physical addressor other value that identifies a physical storage location, such as byusing a disk index 142 (discussed further below). Although reference ismade to “SBN” in the present examples, it is noted that another exampleof a storage location indicator is a block identifier that identifiesblocks and from which a physical storage location can be derived.

The cached block index 124 is stored in a memory 126 of the data storagesystem 102. A “memory” can be implemented with a memory device or acollection of memory devices. Examples of memory devices can include anyor some combination of volatile memory devices (e.g., a dynamic randomaccess memory (DRAM) devices, a static random access memory (SRAM)devices, etc.) and nonvolatile memory devices (e.g., flash memorydevices or other types of nonvolatile memory devices).

A persistent block index 128 is stored at the persistent storage 110.Entries 125 of the cached block index 124 are merged, by the mergeengine 130 of the storage controller 108, into the persistent blockindex 128. In some examples, one cached block index 124 is maintainedper data volume, and one persistent block index 128 is maintained perdata volume. If there are multiple data volumes, then there arerespective multiple cached block indexes 124 and persistent blockindexes 128.

The merge engine 130 can be triggered to perform the merge in responseto an event, such as the cached block index 124 becoming full, or inresponse to a time event that is triggered periodically orintermittently. The cached block index 124 becoming “full” refers to astorage area allocated to the cached block index 124 in the memory 126exceeding a specified threshold.

Entries 125 are added to the cached block index 124 by the block indexupdate logic 122 in response to the incoming data values 114. An entry125 (that contains an LBA mapped to an SBN) is added to the cached blockindex 124 for a given incoming data value 114. The LBA identifies thelogical data block that contains the given incoming data value.

Referring further to FIG. 2, a logical data block 202-1 is written todata volume 1, and a logical data block 202-N is written to data volumeN (where N≥2). In examples according to FIG. 2, the data volumes 1 to Nare part of a domain 210. A domain can include one data volume ormultiple data volumes. In some examples, the domain 210 can be adeduplication domain, where data values stored in the data volume(s) ofthe deduplication domain are subject to data deduplication to remove orreduce duplicate data values. A data storage system can include multipledomains in some examples.

Each logical data block 202-i (i=1-N) is associated with a respectiveLBA and a corresponding data value (an incoming data value 114 in FIG.1). Each data volume 1 to N contains logical data blocks that have beenwritten to the data volume. It is noted that deduplication can beapplied on data values of logical data blocks for just a single datavolume, or alternatively, on data values of logical data blocks formultiple data volumes.

Note that SBNs represent unique data values stored in the data storagesystem 102. More specifically, SBNs provide indications of physicalstorage locations in the physical storage media of the persistentstorage at which the corresponding unique data values 112 are stored.

LBAs in the block index (cached block index 124 and/or persistent blockindex 128) are addresses of the logical data blocks of a data volume.The logical data blocks may or may not contain data values that areduplicative. LBAs of logical data blocks that contain duplicative datavalues are mapped by respective entries of the block index to the sameSBN (that corresponds to a respective data value 112).

In response to a given incoming data value 114, the deduplication engine116 makes a determination of whether a duplicate data value 112 isalready stored at the persistent storage 110. If so, the given incomingdata value 114 is not stored again at the persistent storage 110, toavoid storing of duplicative data values. Instead, deduplication engine116 updates metadata (not shown) stored at the persistent storage 110 toindicate that a new incoming data value 114 has been received thatcorresponds to a given data value 112 already stored at the persistentstorage 110. The metadata can include a count of how many incoming datavalues 114 (corresponding to writes of logical data blocks to a datavolume) are duplicative of the given data value 112.

The data deduplication performed by the deduplication engine 116 isbased on fingerprints generated from the incoming data values 114 by thedata value fingerprint generator 118. A “fingerprint” refers to a valuederived by applying a function on the content of a data value (where the“content” can include the entirety or a sub-portion of the data value).An example of the function that can be applied includes a hash functionthat produces a hash value based on the incoming data value 114.Examples of hash functions include cryptographic hash functions such asthe Secure Hash Algorithm 2 (SHA-2) hash functions, e.g., SHA-224,SHA-256, SHA-384, etc. In other examples, other types of hash functionsor other types of fingerprint functions may be employed.

Fingerprints represent data values stored in the data storage system102. A fingerprint computed for an incoming data value 114 can becompared to fingerprints stored in a fingerprint index 132. Thefingerprint index 132 may be stored at the persistent storage 110. Insome cases, a portion of the fingerprint index 132 may be cached in thememory 126. The fingerprint index 132 includes entries corresponding tofingerprints representing data values 112 stored at the persistentstorage 110. Each entry of the fingerprint index 132 can map afingerprint to an SBN, in some examples.

If the fingerprint computed for an incoming data value 114 matches afingerprint in the fingerprint index 132, then that can be used formaking a determination the incoming data value 114 is duplicative of adata value 112 already stored at the persistent storage 110. If a givenincoming data value 114 is duplicative of a data value 112 alreadystored at the persistent storage 110, then the deduplication engine 116produces a mapping between the LBA of the given incoming data value 114to the SBN of the data value 112 already stored at the persistentstorage 110. This mapping is then added as an entry 125 to the cachedblock index 124.

If a further incoming data value 114 is not duplicative of a data value112 already stored at the persistent storage 110, then the deduplicationengine 116 produces a further mapping between the LBA of the furtherincoming data value 114 to a new SBN that provides an indication of thephysical storage location for the further incoming data value 114. Thisfurther mapping is then added as another entry 125 to the cached blockindex 124.

The data deduplication parameter computation engine 120 includes ahashing logic 134 that receives an SBN from the deduplication engine 116for an incoming data value 114. The hashing logic 134 computes an SBNhash value 138 (FIG. 2) by applying a hash function on the SBN providedby the deduplication engine 116. The hashing function applied on the SBNcan be the same as or different from a hash function applied by the datavalue fingerprint generator 118 on an incoming data value 114.

Although examples depicted in FIG. 1 shows the hashing logic 134 forcomputing hash values based on SBNs, in other examples, the datadeduplication parameter computation engine 120 can include a differenttype of function that when applied on an SBN produces an output valuethat is used for updating a probabilistic cardinality estimator 136. Thecardinality estimator 136 can be stored in the memory 126. In somecases, the cardinality estimator 136 can also be stored at thepersistent storage 110.

As further shown in FIG. 2, each data volume can be associated with acorresponding different cardinality estimator. For example, data volume1 is associated with cardinality estimator 136-1, and data volume N isassociated with cardinality estimator 136-N.

In some examples, the cardinality estimator 136 (FIG. 1) includes a datastructure that contains counts that are useable to derive an estimatedquantity of unique data values stored in the data storage system 102.For example, the data structure can be in the form of an array of countsthat is arranged as rows and columns (FIG. 2 shows that each of thecardinality estimators 136-1 and 136-2 includes an array of entries,where each entry includes a respective count).

Although reference is made to a cardinality estimator arranged as anarray of counts in some examples, it is noted that a probabilisticcardinality estimator may have other forms in other examples.

In accordance with some implementations of the present disclosure, acount in an entry of the cardinality estimator 136 is updated inresponse to either an addition of a data value that maps to the entry,or a deletion of a data value that maps to the entry. This ability toupdate a count in an entry of the cardinality estimator 136 in responseto additions and removals of data values provides the capability of thedata deduplication parameter computation engine 120 in computingparameters relating to data deduplication while the data storage system102 is online.

As shown in FIG. 2, the SBN hash value 138 produced by the hashing logic134 of the data deduplication parameter computation engine 120 for theincoming data value of the logical data block 202-1 maps to an entry 204of the cardinality estimator 136-1. For example, the SBN hash value 138can be divided into a first hash portion 138-A that maps to a respectivecolumn of the cardinality estimator 136-1, and a second hash portion138-B that maps to a respective row of the cardinality estimator 136-1.The intersection of the respective column and the respective row mappedby the hash portions 138-A and 138-B is the entry 204 that contains acount. Different values of the first hash portion 138-A map to differentcolumns of the cardinality estimator 136-1, and different values of thesecond hash portion 138-B can map to different rows of the cardinalityestimator 136-1.

In some examples, the second hash portion 138-B does not map directly toa row of the cardinality estimator 136-1. Rather, the data deduplicationparameter computation engine 120 can determine how many trailing zeros(a quantity of trailing zeros) are in the second hash portion 138-B.This quantity of trailing zeros of the second hash portion 138-B maps tothe respective row of the cardinality estimator 136-1. For example, 0trailing zeros of the second hash portion 138-B maps to row 0 of thecardinality estimator 136-1, 1 trailing zero of the second hash portion138-B maps to row 1 of the cardinality estimator 136-1, 2 trailing zerosof the second hash portion 138-B maps to row 2 of the cardinalityestimator 136-1, and so forth.

A quantity of trailing zeros of the second hash portion 138-B refers tohow many zeros are in the least significant bits of the second hashportion 138-B.

The cardinality estimator 136 can also be referred to as a countingcardinality estimator, since counts are maintained in the cardinalityestimator 136 to track a quantity of occurrences of SBNs that have acorresponding characteristic (which in the above example is the numberof trailing zeros in an SBN hash value produced from a respective SBN).

Table 1 below provides an example of an SBN hash value 138 produced froman SBN having an example value “A”.

TABLE 1 SBN A SBN Hash Value 01101010101000 First Hash Portion 0110Second Hash Portion 1010101000 Trailing Zeroes in Second Hash 3 Portion

According to Table 1, the hashing logic 134 computes a corresponding SBNhash value (having a binary sequence 01101010101000) based on the SBNthat has value “A”.

The SBN hash value (01101010101000) is split into a first hash portion138-A, which in the example of Table 1 is the first 4 bits of the SBNhash value, i.e., 0110. The second hash portion 138-B includes theremaining bits of the SBN hash value (1010101000).

The data deduplication parameter computation engine 120 counts aquantity of trailing zeros in the second hash portion 138-B. In theexample of Table 1, the quantity of trailing zeros is 3, which can mapto a respective row (e.g., row 3) of the cardinality estimator. Thefirst hash portion 138-A (0110) maps to a respective column of thecardinality estimator.

In alternative examples, the first hash portion 138-A maps to a row of acardinality estimator, and the second hash portion 138-B (or morespecifically, a quantity of zeros in the second hash portion 138-B) mapsto a column of the cardinality estimator.

As incoming data values 114 are stored in the data storage system 102that result in SBNs added to the block index (either the cached blockindex 124 or the persistent block index 128), respective counts in thecardinality estimator are incremented.

It is also possible that requests received by the storage controller 108can result in deletion of data values 112 from the data storage system102. A data value 112 is deleted from the persistent storage 110 ifdeletion operations result in no instances of the data value 112remaining at the persistent storage 110. When the data value 112 isdeleted, the corresponding SBN is also deleted. The deduplication engine116 can provide an indication to the data deduplication parametercomputation engine 120 whenever an SBN is deleted. The datadeduplication parameter computation engine 120 can detect deletion of anSBN, and can cause a corresponding count in an entry of the cardinalityestimator 136 to be decremented.

In alternative examples, instead of incrementing a count in a mappedentry of the cardinality estimator 136 in response to an addition of anSBN to the block index, the count can instead by decremented. In suchalternative examples, in response to a deletion of an SBN from the blockindex, the count in the mapped entry of the cardinality estimator 136 isincremented.

More generally, a count in the mapped entry of the cardinality estimator136 is advanced (one of incrementing or decrementing) in response to anaddition of an SBN, and the count in the mapped entry of the cardinalityestimator 136 is reversed (a different one of incrementing ordecrementing) in response to a removal of an SBN.

Based on the counts in the cardinality estimator 136-1, the datadeduplication parameter computation engine 120 computes a parameterrelating data deduplication (“data deduplication parameter 140” in FIG.2). There can be multiple different data deduplication parameters 140computed by the data deduplication parameter computation engine 120. Thedata deduplication parameter(s) 140 can be stored in the memory 126 (andpossibly may also be stored in the persistent storage 110).

A similar technique can be applied to compute a parameter relating todata deduplication for data volume N.

The persistent storage 110 also stores a disk index 142, which includesentries 143 that map corresponding SBNs to respective physical storagelocation identifiers, such as physical addresses (“ADDR” in FIG. 1). Toretrieve a data value 112 corresponding to an SBN, the storagecontroller 108 can access the corresponding entry 143 of the disk index142, where the entry 143 contains the physical storage locationidentifier corresponding to the SBN. The physical storage locationidentifier is then used by the storage controller 108 to retrieve thecorresponding data value 112.

Table 3 below shows an example in which the operations 1 to 12 involveadditions of SBNs, and operation 13 involves the deletion of an SBN. Forexample, operation 1 adds SBN “A”, operation 2 adds SBN “B”, and soforth.

TABLE 2 Hash Hash Trailing SBN Portion Portion Zeros Operation SBN HashA (Column) B (Row) 1 Add A 0010100 00 (0) 10100 2 2 Add B 1001001 10 (2)01001 0 3 Add C 0100100 01 (1) 00100 2 4 Add A 0010100 00 (0) 10100 2 5Add D 1101010 11 (3) 01010 1 6 Add B 1001001 10 (2) 01001 0 7 Add A0010100 00 (0) 10100 2 8 Add E 0011101 00 (0) 11101 0 9 Add F 0111000 01(1) 11000 3 10 Add B 1001001 10 (2) 01001 0 11 Add A 0010100 00 (0)10100 2 12 Add G 1010110 10 (2) 10110 1 13 Delete A 0010100 00 (0) 101002

Updates of the cardinality estimator 136 in response to the operationsare shown in FIGS. 3A-3G.

In response to operation 1 that adds SBN “A”, the corresponding SBN hashvalue (0010100) maps to an entry 302 of the cardinality estimator 136.Specifically, the first hash portion (e.g., 00) maps to column 0 (306-0)of the cardinality estimator 136, and the second hash portion (10100),which has 2 trailing zeros, maps to row 2 (304-2) that corresponds to 2trailing zeros. The data deduplication parameter computation engine 120increments the count in the entry 302 (from 0 to 1).

The cardinality estimator 136 has rows 304-0 to 304-5 (that map tosecond hash portion values with 0 to 5 trailing zeros, respectively),and columns 306-0 to 306-3 (that map to first hash portion values 0 to3, respectively).

Although FIGS. 3A-3F show the cardinality estimator 136 with specificnumbers of rows and columns, the cardinality estimator 136 can haveother numbers of rows and columns in other examples.

Operation 2 adds SBN “B”, which produces an SBN hash value (1001001)that maps to an entry 308 in row 304-0 and column 306-2 (FIG. 3B). Thedata deduplication parameter computation engine 120 increments the countin the entry 308 (from 0 to 1).

Operation 3 adds SBN “C” which produces an SBN hash value (0100100) thatmaps to an entry 310 in row 304-2 and column 306-1 (FIG. 3C). The datadeduplication parameter computation engine 120 increments the count inthe entry 310 (from 0 to 1).

Operation 4 again adds SBN “A”, which was also added in operation 1. Asa result, the count in the entry 302 is incremented again (from 1 to 2)by the data deduplication parameter computation engine 120.

FIG. 3E shows an update of the cardinality estimator 136 in response tooperation 5.

Remaining operations 6-11 result in respective updates (increments) ofcounts in corresponding entries of the cardinality estimator 136.

FIG. 3G shows an update of the cardinality estimator 136 in response tooperation 12.

Operation 13 removes an occurrence of SBN “A”, which causes the datadeduplication parameter computation engine 120 to decrement the count inthe entry 302, from the value 4 in FIG. 3F to the value 3 in FIG. 3G.

The quantity of SBNs for a data volume can be estimated based on thequantity of trailing zeros of an SBN hash value. The quantity of SBNseffectively represents a quantity of unique data values (112 in FIG. 1)that are stored in the data storage system 102 for the data volume.

If a given SBN hash value has NT trailing zeros, then the estimatedquantity of SBNs is computed as 2^(NT). The foregoing is based on theassumption that about ½ of SBN hash values have 0 trailing zeros, about¼ of SBN hash values have 1 trailing zero, about ⅛ of SBN hash valueshave 2 trailing zeros, and so forth. Note that the estimate of thequantity of SBNs based on the number of trailing zeros of an SBN hashvalue is a rough estimate, which may be inaccurate. To compensate forsuch inaccuracy, the quantity of SBNs is computed by the datadeduplication parameter computation engine 120 using multiple SBN hashvalues that correspond to the multiple columns of the cardinalityestimator 136 (e.g., 4 columns in FIGS. 3A-3G).

Each of the columns of the cardinality estimator 136 can be stored in arespective register. For example, 4 registers can be used to store the 4columns of the cardinality estimator 136 of FIGS. 3A-3G.

The computation of the quantity of SBNs can occur at any time during theoperation of the data storage system 102, such as at any of the timesthat correspond to operations 1 to 13 in Table 2.

The following assumes that the computation of the quantity of SBNsoccurs after operation 13, and is based on the updated cardinalityestimator 136 shown in FIG. 3G.

Within each column (register), the data deduplication parametercomputation engine 120 determines the highest (maximum) order SBN hashvalue observed. The maximum order of observed SBN hash values in acolumn of the cardinality estimator 136 is 1 plus the maximum quantityof trailing zeros observed in the column (1+NT, where NT represents themaximum quantity of trailing zeros observed in the column). For example,in column 306-0 of FIG. 3G, two non-zero entries are observed inrespective rows 304-0 and 304-2. The maximum order of observed SBN hashvalues in the column 306-0 of FIG. 3G is 1+2 (=3). Similarly, themaximum order of observed SBN hash values in the column 306-1 of FIG. 3Gis 1+3 (=4), the maximum order of observed SBN hash values in the column306-2 of FIG. 3G is 1+1 (=2), and the maximum order of observed SBN hashvalues in the column 306-3 of FIG. 3G is 1+1 (=2).

The data deduplication parameter computation engine 120 then computesthe harmonic mean (H) of the maximum orders of the multiple registers (mregisters, where m≥2), according to:

${H = ( {\sum\limits_{j = 1}^{m}2^{{- M}{O{\lbrack j\rbrack}}}} )^{- 1}},$

where MO[j] is the maximum order of register j (column j in thecardinality estimator 136). In FIG. 3G, m=4.

In some examples, to compensate for hash collisions in the SBN hashvalues, a correction factor (b_(m)m²) can be applied to the harmonicmean (Z) as follows:

C=b _(m)m²Z.

The value of b_(m) can be based on the value of m, and can beempirically derived based on expected hash collisions.

The value of C is the estimate of the quantity of SBNs in the datastorage system 102, which represents an estimate of the quantity ofunique data values 112 stored in the data storage system 102.

In some examples, each count in an entry of the cardinality estimator136 is maintained using a counter that is incremented in response toaddition of an SBN that maps to the entry, and decremented in responseto removal of an SBN that maps to the entry.

For improved efficiency, the number of bits used for the counter can bereduced. For example, an 8-bit counter can be used. Increasing thenumber of bits of the counter can improve accuracy in estimating thequantity of SBNs, but comes at the expense of increased processing andstorage overhead, since larger counters consume more storage space andproduce larger values that may be more processing intensive.

Using a smaller counter may result in overflow, where the counter canincrement to a maximum value that can be held by the counter, afterwhich another addition of an SBN that maps to the entry of the countercan cause the counter to reset to zero (an overflow condition). Sincethe data deduplication parameter computation engine 120 estimates thequantity of SBNs based on multiple registers (columns of the cardinalityestimator 136), the overflow condition associated with one of themultiple registers may still provide for an estimate of the quantity ofSBNs of acceptable accuracy.

In some examples, for further efficiency from a storage and processingperspective, the cardinality estimator 136 can track SBN hash valuesthat have a specified range of trailing zeros; i.e., the cardinalityestimator 136 does not track SBN hash values outside of the specifiedrange. For example, the cardinality estimator 136 can track SBN hashvalues with greater than equal 10 trailing zeros, and less than or equal40 trailing zeros. SBN hash values with less than 10 trailing zeros andgreater than 40 trailing zeros are ignored and not tracked by thecardinality estimator 136.

In some examples, instead of updating the cardinality estimator 136 aseach incoming data value 114 and a respective entry is added to thecached block index 124 for a data volume, the cardinality estimator 136is updated in response to a merge of the cached block index 124 to thepersistent block index 128 by the merge engine 130. This can reduce thefrequency of cardinality estimator updates to reduce the processing loadassociated with computing parameters relating to data deduplication.

The cardinality estimator 136 can also be updated in response toremovals of SBNs from the persistent block index 128.

By updating the cardinality estimator 136 responsive to merging of thecached block index 124 and to removals of SBNs from the persistent blockindex 128, the cardinality estimator 136 can stay consistent with thepersistent block index 128.

The estimated quantity of SBNs is an example of a parameter relating todata deduplication that can be computed by the data deduplicationparameter computation engine 120.

The data deduplication parameter computation engine 120 can also computea deduplication ratio (PDR) as another example parameter relating todata deduplication, by dividing the quantity of logical data blocks(Q(logical data blocks)) written to a data volume by the quantity ofSBNs (C):

PDR=Q(logical data blocks)/C.

Another example parameter relating to data deduplication that can becomputed by the data deduplication parameter computation engine 120 is aparameter representing a quantity of physical blocks (PReclaim) that canbe reclaimed in the persistent storage 110 if data volume i weredeleted. This parameter is based on determining a quantity of SBNspresent in data volume i but not in any other data volume, computed as:

PReclaim=SBNs_in_domain−Σ_(j≠i) C _(j).

In the equation above, SBNs_in_domain represents the quantity of SBNs ina domain of the data storage system 102, where the domain includesmultiple data volumes. SBNs_in_domain can be computed by summing C₁, C₂,. . . , C_(N), where N represents the quantity of data volumes in thedomain, and C₁, C₂, . . . , C_(N) represent the estimated quantities ofSBNs computed for respective data volumes 1, 2, . . . , N usingcorresponding cardinality estimators 136. In other examples, the valueof SBNs_in_domain may be computed using a different technique.

The sum Σ_(j≠i) C_(j) is the sum of the estimated quantities of SBNs forall data volumes in the domain except data domain i.

Another example parameter relating to data deduplication that can becomputed by the data deduplication parameter computation engine 120 is aparameter (PSimilarity) representing similarity of multiple datavolumes.

Assume an example where a first cardinality estimator (E1) (an exampleof 136 discussed above) is used to produce a value C1 that is theestimate of the quantity of SBNs in data volume V1, and a secondcardinality estimator (E2) (an example of 136 discussed above) is usedto produce a value C2 that is the estimate of the quantity of SBNs indata volume V2.

A simple example is given below that assumes that the data volume V1 has100 logical data blocks, and the data volume V2 also has 100 logicaldata blocks. In the example, it is assumed that C1 has value 20, whichindicates that the data volume V1 is estimated to have 20 unique datavalues. Also, C2 also has value 20, which indicates that the data volumeV2 is estimated to have 20 unique data values.

In the above example, the deduplication ratio PDR1 for the data volumeV1 is 5 (100/20), and the deduplication ratio PDR2 for the data volumeV2 is also 5 (100/20).

To determine the similarity of the content of the data volumes V1 andV2, a third cardinality estimator E3 is derived by combining (taking theunion) of the cardinality estimator E1 and E2. The union of E1 and E2 isbasically a combination of the arrays of counts of E1 and E2 where thecounts of corresponding entries in E1 and E2 are added together. If allthe data values of the data volume V1 are the same as the data values ofthe data volume V2, then the cardinality estimator E3 should produce aC3 value of 20. If all the data values of the data volume V1 aredifferent from the data values of the data volume V2, then thecardinality estimator E3 should produce a C3 value of 40. If thecardinality estimator E3 produces a C3 value between 20 and 40, thenthat indicates that some data values are shared between V1 and V2, andsome data values are different between V1 and V2. A value closer to 20indicates more shared data values, and a value closer to 40 indicatesless shared data values.

Although the above simple example assumes the same number of logicaldata blocks and unique data values in V1 and V2, other examples wouldinclude data volumes with different numbers of logical data blocks anddifferent number of unique data values.

More generally, to determine the similarity of multiple data volumes,the cardinality estimators for the multiple data volumes are combined toproduce a combined cardinality estimator. The combined cardinalityestimator produces an estimate of a quantity of unique data values,where this estimated quantity is compared to a range of quantities ofunique data values that starts at a first quantity value indicating thatthe multiple data volumes share all data values, and ends at a secondquantity value indicating that the multiple data volumes do not shareany data values. The position of the estimated quantity in the rangeprovides an indication of similarity of the multiple data volumes.

The various parameters relating to data deduplication that are computedby the data deduplication parameter computation engine 120 can be usedin any of a number of ways. For example, the parameters relating to datadeduplication can be used to determine the performance of the datastorage system 102 in applying data deduplication for each respectivedata volume. For example, if the data deduplication ratio for a givendata volume is low (which means that a relatively large number of datavalues 112 are stored relative to the number of logical data blockswritten to the given data volume), then the data storage system 102 canprovide a recommendation to delete the given data volume, such as incases where storage space is running low. A decision by the data storagesystem 102 to delete a data volume or a group of data volumes (oralternatively to move the data volume or group of data volumes toanother domain or another data storage system) can be based on theparameter PReclaim representing a quantity of physical blocks that canbe reclaimed in the persistent storage 110 if each respective datavolume were deleted. For example, values of the parameter PReclaim formultiple data volumes in a group of data volumes can be combined (e.g.,added together) to determine the total number of physical blocks thatcan be reclaimed from deleting (or moving) the group of data volumes.The parameters can also be used to estimate how much physical spacewould be consumed if a data volume were moved to another domain.Additionally, the data storage system 102 can provide an indication thatmultiple data volumes share similar content.

The foregoing refers to using cardinality estimators that are updated asSBNs are added and removed.

In further examples, a further cardinality estimator (similar to thosedepicted in FIGS. 3A-3G) for example, can be maintained that maps tohash values computed based on data values, rather than SBN hash values.For example, the hash values (referred to as “data hash values”) basedon data values can be computed by the data value fingerprint generator118 of FIG. 1.

Each data hash values can similarly be separated into a first hashportion and a second hash portion (similar to 138-A and 138-B discussedabove), which map to a respective column and respective row of thefurther cardinality estimator. Counts in the further cardinalityestimator can be updated as data values are added and removed. Thestorage controller 108 (or a remote computer) can compare the furthercardinality estimator computed based on the data values of the datastorage system 102 with another further cardinality estimator computedbased on the data values of another data storage system. This comparisoncan reveal the similarity of the different data storage systems in termsof how similar the data values are in the different data storagesystems.

FIG. 4 is a block diagram of a non-transitory machine-readable orcomputer-readable storage medium 400 storing machine-readableinstructions that upon execution cause a system to perform varioustasks. The system can include a computer or a collection of computers.For example, the system can include the storage controller 108 of FIG.1.

The machine-readable instructions include value computation instructions402 to compute respective values (e.g., SBN hash values) forcorresponding data value indicators (e.g., SBNs) added to and removedfrom a deduplication data store in which duplicated data values havebeen eliminated. Each respective data value indicator of the data valueindicators represents presence of a unique data value in thededuplication data store. A “deduplication data store” refers to a datastore, such as implemented using the data storage system 102, in whichdata deduplication is applied to reduce or avoid the storing ofduplicate data values.

The machine-readable instructions include estimator update instructions404 to update an estimator (e.g., the probabilistic cardinalityestimator 136 of FIG. 1) based on the respective values, to reflect anaddition of a first data value indicator (e.g., first SBN) to thededuplication data store and a removal of a second data value indicator(e.g., second SBN) from the deduplication data store.

The machine-readable instructions include data deduplication parametercomputation instructions 406 to compute, using the updated estimator, aparameter relating to data deduplication at the deduplication datastore.

In some examples, the machine-readable instructions compute a firstvalue (e.g., a first SBN hash value) based on the first data valueindicator, and update an entry of the estimator (e.g., by incrementing acount in the entry) based on the first value indicator, to reflect theaddition of the first data value indicator to the deduplication datastore. The machine-readable instructions compute a second value (e.g., asecond SBN hash value) based on the second data value indicator, andupdate the entry of the estimator (e.g., by decrementing the count inthe entry) based on the second value, to reflect the removal of thesecond data value indicator from the deduplication data store.

In some examples, the estimator includes an array of entries, where eachcorresponding value of the respective values (e.g., SBN hash values)maps to a corresponding entry of the array of entries. The updating ofthe estimator includes updating a count in the corresponding entry basedon the corresponding value, the count representing how many of therespective values map to the corresponding entry.

In some examples, the corresponding value (e.g., an SBN hash value)includes a first value portion (e.g., 138-A in FIG. 2) and a secondvalue portion (e.g., 138-B in FIG. 2), the first value portion mappingto a column of the array of entries, and the second value portion (morespecifically, a quantity of trailing zeros of the second value portion)mapping to a row of the array of entries.

In some examples, a position of a first entry containing a non-zerocount in the array of entries indicates a first estimate of how manyunique data values are in a portion of the deduplication data store, anda position of a second entry containing a non-zero count in the array ofentries indicates a second estimate of how many unique data values arein the portion of the deduplication data store. The first entry can bein a first column of the array of entries, and the second entry can bein a second column of the array of entries. The parameter can becomputed based on the first estimate and the second estimate, such as bycomputing a harmonic mean based on maximum orders in correspondingcolumns of the cardinality estimator 136 discussed further above.

In some examples, the estimator tracks hash values with quantities oftrailing zeros within a specified range, and the estimator does nottrack hash values with quantities of trailing zeros outside thespecified range.

FIG. 5 is a block diagram of a system 500, which can include a computeror a collection of computers. For example, the system 500 can be thedata storage system 102 of FIG. 1, or the storage controller 108 of FIG.1.

The system 500 includes a hardware processor 502 (or alternatively,multiple hardware processors. A hardware processor can include amicroprocessor, a core of a multi-core microprocessor, amicrocontroller, a programmable integrated circuit, a programmable gatearray, or another hardware processing circuit.

The system 500 includes a non-transitory storage medium 504 storingmachine-readable instructions executable on the hardware processor 502to perform various tasks. Machine-readable instructions executable on ahardware processor can refer to the instructions executable on a singlehardware processor or the instructions executable on multiple hardwareprocessors.

The machine-readable instructions in the storage medium 504 includevalue computation instructions 506 to compute respective values (e.g.,SBN hash values) for corresponding storage location indicators (e.g.,SBNs) added to and removed from a deduplication data store in whichduplicated data values have been eliminated. Each respective storagelocation indicator of the storage location indicators represents arespective storage location of a unique data value in the deduplicationdata store.

The machine-readable instructions in the storage medium 504 includeestimator update instructions 508 to update an estimator based on therespective values, to reflect an addition of a first storage locationindicator to the deduplication data store and a removal of a secondstorage location indicator from the deduplication data store.

The machine-readable instructions in the storage medium 504 include datadeduplication parameter computation instructions 510 to compute, usingthe updated estimator, a parameter relating to data deduplication at thededuplication data store.

FIG. 6 is a flow diagram of a process 600, which can be performed by thestorage controller 108 or another machine.

The process 600 computes (at 602) respective hash values based oncontent of corresponding storage location indicators added to andremoved from a deduplication data store in which duplicated data valueshave been eliminated, where each respective storage location indicatorof the storage location indicators represents a respective storagelocation of a unique data value in the deduplication data store.

The process 600 updates (at 604) a probabilistic cardinality estimatorbased on the respective hash values, to reflect an addition of a firststorage location indicator to the deduplication data store and a removalof a second storage location indicator from the deduplication datastore. The probabilistic cardinality estimator includes counters, andthe updating includes advancing a first counter of the countersresponsive to the addition of the first storage location indicator, andreversing the first counter responsive to the removal of the secondstorage location indicator.

The process 600 computes (at 606), using the updated probabilisticcardinality estimator, a parameter relating to data deduplication at thededuplication data store.

A storage medium (e.g., 400 in FIG. 4 or 504 in FIG. 5) can include anyor some combination of the following: a semiconductor memory device suchas a dynamic or static random access memory (a DRAM or SRAM), anerasable and programmable read-only memory (EPROM), an electricallyerasable and programmable read-only memory (EEPROM) and flash memory orother type of non-volatile memory device; a magnetic disk such as afixed, floppy and removable disk; another magnetic medium includingtape; an optical medium such as a compact disk (CD) or a digital videodisk (DVD); or another type of storage device. Note that theinstructions discussed above can be provided on one computer-readable ormachine-readable storage medium, or alternatively, can be provided onmultiple computer-readable or machine-readable storage media distributedin a large system having possibly plural nodes. Such computer-readableor machine-readable storage medium or media is (are) considered to bepart of an article (or article of manufacture). An article or article ofmanufacture can refer to any manufactured single component or multiplecomponents. The storage medium or media can be located either in themachine running the machine-readable instructions, or located at aremote site from which machine-readable instructions can be downloadedover a network for execution.

In the foregoing description, numerous details are set forth to providean understanding of the subject disclosed herein. However,implementations may be practiced without some of these details. Otherimplementations may include modifications and variations from thedetails discussed above. It is intended that the appended claims coversuch modifications and variations.

What is claimed is:
 1. A non-transitory machine-readable storage mediumcomprising instructions that upon execution cause a system to: computerespective values for corresponding data value indicators added to andremoved from a deduplication data store in which duplicated data valueshave been eliminated, wherein each respective data value indicator ofthe data value indicators represents presence of a unique data value inthe deduplication data store; update an estimator based on therespective values, to reflect an addition of a first data valueindicator to the deduplication data store and a removal of a second datavalue indicator from the deduplication data store; and compute, usingthe updated estimator, a parameter relating to data deduplication at thededuplication data store.
 2. The non-transitory machine-readable storagemedium of claim 1, wherein the instructions upon execution cause thesystem to: compute a first value based on the first data valueindicator; update an entry of the estimator based on the first value, toreflect the addition of the first data value indicator to thededuplication data store; compute a second value based on the seconddata value indicator; and update the entry of the estimator based on thesecond value, to reflect the removal of the second data value indicatorfrom the deduplication data store.
 3. The non-transitorymachine-readable storage medium of claim 2, wherein the updating of theentry of the estimator based on the first value comprises advancing acount in the entry, and wherein the updating of the entry of theestimator based on the second value comprises reversing the count in theentry.
 4. The non-transitory machine-readable storage medium of claim 1,wherein the parameter represents how many unique data values are presentin a portion of the deduplication data store.
 5. The non-transitorymachine-readable storage medium of claim 1, wherein the parameterrepresents a similarity between a plurality of data volumes of thededuplication data store.
 6. The non-transitory machine-readable storagemedium of claim 1, wherein the parameter represents how many unique datavalues are present in a first portion of the deduplication data storeand not present in any other portion of the deduplication data store. 7.The non-transitory machine-readable storage medium of claim 1, whereinthe parameter represents a deduplication ratio of a portion of thededuplication data store.
 8. The non-transitory machine-readable storagemedium of claim 1, wherein the parameter represents how many physicalblocks of the deduplication data store would be reclaimed responsive toa deletion of a portion of the deduplication data store.
 9. Thenon-transitory machine-readable storage medium of claim 1, wherein theestimator comprises an array of entries, and wherein each correspondingvalue of the respective values maps to a corresponding entry of thearray of entries, and wherein updating the estimator comprises updatinga count in the corresponding entry based on the corresponding value, thecount representing how many of the respective values map to thecorresponding entry.
 10. The non-transitory machine-readable storagemedium of claim 9, wherein the corresponding value comprises a firstvalue portion and a second value portion, the first value portionmapping to a column of the array of entries, and the second valueportion mapping to a row of the array of entries.
 11. The non-transitorymachine-readable storage medium of claim 10, wherein a position of afirst entry containing a non-zero count in the array of entriesindicates a first estimate of how many unique data values are in aportion of the deduplication data store.
 12. The non-transitorymachine-readable storage medium of claim 11, wherein a position of asecond entry containing a non-zero count in the array of entriesindicates a second estimate of how many unique data values are in theportion of the deduplication data store, wherein the computing of theparameter is based on the first estimate and the second estimate. 13.The non-transitory machine-readable storage medium of claim 1, whereinthe respective values comprise hash values, and wherein a quantity oftrailing zeros in each hash value of the hash values indicates anestimate of how many unique data values are in a portion of thededuplication data store.
 14. The non-transitory machine-readablestorage medium of claim 13, wherein the estimator tracks hash valueswith quantities of trailing zeros within a specified range, and theestimator does not track hash values with quantities of trailing zerosoutside the specified range.
 15. A system comprising: a processor; and anon-transitory storage medium storing instructions executable on theprocessor to: compute respective values for corresponding storagelocation indicators added to and removed from a deduplication data storein which duplicated data values have been eliminated, wherein eachrespective storage location indicator of the storage location indicatorsrepresents a respective storage location of a unique data value in thededuplication data store; update an estimator based on the respectivevalues, to reflect an addition of a first storage location indicator tothe deduplication data store and a removal of a second storage locationindicator from the deduplication data store; and compute, using theupdated estimator, a parameter relating to data deduplication at thededuplication data store.
 16. The system of claim 15, wherein theestimator comprises an array of entries, each respective entry of thearray of entries comprising a respective counter, and wherein theinstructions are executable on the processor to: increment a counter ina given entry of the array of entries in response to the addition of thefirst storage location indicator, wherein a first value computed for thefirst storage location indicator maps to the given entry; and decrementthe counter in the given entry in response to the removal of the secondstorage location indicator, wherein a second value computed for thesecond storage location indicator maps to the given entry.
 17. Thesystem of claim 16, wherein the first value comprises a first portionand a second portion, the first portion mapping to a column of the arrayof entries, and a quantity of trailing zeros in the second portionmapping to a row of the array of entries.
 18. The system of claim 16,wherein the computing of the parameter relating to data deduplication atthe deduplication data store is based on non-zero counts maintained bythe counters in the array of entries.
 19. A method of a systemcomprising a hardware processor, comprising: computing respective hashvalues based on content of corresponding storage location indicatorsadded to and removed from a deduplication data store in which duplicateddata values have been eliminated, wherein each respective storagelocation indicator of the storage location indicators represents arespective storage location of a unique data value in the deduplicationdata store; updating a probabilistic cardinality estimator based on therespective hash values, to reflect an addition of a first storagelocation indicator to the deduplication data store and a removal of asecond storage location indicator from the deduplication data store,wherein the probabilistic cardinality estimator comprises counters, andthe updating comprises advancing a first counter of the countersresponsive to the addition of the first storage location indicator, andreversing the first counter responsive to the removal of the secondstorage location indicator; and compute, using the updated probabilisticcardinality estimator, a parameter relating to data deduplication at thededuplication data store.
 20. The method of claim 19, furthercomprising: computing a further probabilistic cardinality estimatorupdated responsive to hash values computed based on data values storedin the deduplication data store, the further probabilistic cardinalityestimator when compared to another probabilistic cardinality estimatorbased on data values stored in another deduplication data storeproviding an indication of similarity between the deduplication datastore and the another deduplication data store.